Pillar design by combining finite element methods, neural networks and reliability: A case study of the Feng Huangshan copper mine, China

Abstract

This paper presents a mine pillar design approach by combining finite element methods (FEMs), neural networks (NN) and reliability analysis. This practical approach is presented by examining an actual cylindrical mine pillar in a copper mine and taking into account uncertainties in ore pillar material parameters including modulus, Poisson's ratio, density and uniaxial compressive strength. The ore pillar had to be able to safely and effectively support a drilling room that occupied an open space of 3.8 m high and 55 m long and 20 m wide and at a depth of 360 m below ground surface. Three-dimensional FEM was used to simulate the mining operations and to estimate average pillar compressive stress at each operation step. A pillar performance function was established in implicit form taking into account pillar strength and pillar dimension. NN was incorporated in the FEM to substantially reduce the number of finite element calculations in establishment of the relationship between pillar compressive stress and basic random variables. Trained NN was then used to generate a database for the implicit performance function. The database was used to determine the reliability index and failure probability for each trial pillar diameter. Relationship between pillar reliability index and each of the coefficients of variation of the basic random variables was used for optimal design of pillar diameter. The optimal pillar design was used in the mining construction and functioned well. © 2003 Elsevier Science Ltd. All rights reserved.